diff options
Diffstat (limited to 'python/mark_backtest_underpar.py')
| -rw-r--r-- | python/mark_backtest_underpar.py | 240 |
1 files changed, 157 insertions, 83 deletions
diff --git a/python/mark_backtest_underpar.py b/python/mark_backtest_underpar.py index a7d72c2b..401ef81c 100644 --- a/python/mark_backtest_underpar.py +++ b/python/mark_backtest_underpar.py @@ -3,162 +3,236 @@ import matplotlib import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm -from statsmodels.formula.api import gls import seaborn as sb +import datetime + +from statsmodels.formula.api import gls -from db import serenitas_engine, dawn_engine, DataError +from utils.db import serenitas_engine, dawn_engine, DataError import globeop_reports as ops -def get_mark_df(asset_class = 'Subprime'): - #It used to be >1000 but as of 12/17/2018 changed it to 3000 - #Filter marks >3000 where the marks are weird... - df_external_marks = pd.read_sql_query("select * from external_marks_mapped where mark < 3000" - , dawn_engine) + +def get_mark_df(asset_class="Subprime"): + # It used to be >1000 but as of 12/17/2018 changed it to 3000 + # Filter marks >3000 where the marks are weird... + df_external_marks = pd.read_sql_query( + "select * from external_marks_mapped where mark < 3000", dawn_engine + ) df_trades = pd.DataFrame() for date in df_external_marks.date.unique(): - df_temp= pd.read_sql_query("select description, identifier, usd_market_value/price*100 as curr_ntl from risk_positions(%s, %s) where price >0 and length(identifier) = 9 " - , dawn_engine, params = [date, asset_class]) - df_temp['date'] = date + df_temp = pd.read_sql_query( + "select description, identifier, usd_market_value/price*100 as curr_ntl from risk_positions(%s, %s) where price >0 and length(identifier) = 9 ", + dawn_engine, + params=[date, asset_class], + ) + df_temp["date"] = date df_trades = df_trades.append(df_temp) df = df_trades.merge(df_external_marks).dropna() - return df.set_index(['date','identifier']) + return df.set_index(["date", "identifier"]) + -def calc_mark_diff(df, sources= ['PRICESERVE', 'PRICINGDIRECT','BVAL','MARKIT','BROKER', 'REUTERS', 'S&P', 'IDC']): +def calc_mark_diff( + df, + sources=[ + "PRICESERVE", + "PRICINGDIRECT", + "BVAL", + "MARKIT", + "BROKER", + "REUTERS", + "S&P", + "IDC", + ], +): - #All Sources (including manager...?!) - average, manager mark only, median, closest - g = df.groupby(level = ['date','identifier']) + # All Sources (including manager...?!) - average, manager mark only, median, closest + g = df.groupby(level=["date", "identifier"]) diff = g.mean() - diff = diff.join(df[df.source == 'MANAGER']['mark'], rsuffix = '_manager') - diff = diff.join(g.median()['mark'], rsuffix = '_median_all') + diff = diff.join(df[df.source == "MANAGER"]["mark"], rsuffix="_manager") + diff = diff.join(g.median()["mark"], rsuffix="_median_all") temp = g.apply(closest) - temp = temp.rename('mark_closest_all') + temp = temp.rename("mark_closest_all") diff = diff.join(temp) - #Filtered Sources - mean, median, remove max min + # Filtered Sources - mean, median, remove max min df_filtered = df[df.source.isin(sources)] - g1 = df_filtered.groupby(level = ['date','identifier']) - diff = diff.join(g1.mean()['mark'], rsuffix = '_filtered_mean') - diff = diff.join(g1.median()['mark'], rsuffix = '_filtered_median') - diff = diff.join(g1.mark.apply(remove_max_min), rsuffix = '_filtered_no_max_min') + g1 = df_filtered.groupby(level=["date", "identifier"]) + diff = diff.join(g1.mean()["mark"], rsuffix="_filtered_mean") + diff = diff.join(g1.median()["mark"], rsuffix="_filtered_median") + diff = diff.join(g1.mark.apply(remove_max_min), rsuffix="_filtered_no_max_min") - #calculate difference: negative means Serenitas marks higher - diff = diff.multiply(diff.curr_ntl/100, axis = 'index') - del diff['curr_ntl'] - diff = diff.rename(columns = {'mark':'mark_mean_all'}) - diff = diff.apply(lambda x: (x-x.mark_manager), axis = 1) + # calculate difference: negative means Serenitas marks higher + diff = diff.multiply(diff.curr_ntl / 100, axis="index") + del diff["curr_ntl"] + diff = diff.rename(columns={"mark": "mark_mean_all"}) + diff = diff.apply(lambda x: (x - x.mark_manager), axis=1) + + return diff.groupby(level="date").sum() - return diff.groupby(level = 'date').sum() def closest(x): if x.mark.count() > 1: - x['dist'] = abs(x.mark - x.mark[x.source == 'MANAGER']) - return x.mark[x.dist == x.dist[x.dist>0].min()].iloc[0] + x["dist"] = abs(x.mark - x.mark[x.source == "MANAGER"]) + return x.mark[x.dist == x.dist[x.dist > 0].min()].iloc[0] else: return x.mark[0] + def remove_max_min(x): if x.count() >= 4: - return (x.sum() - x.max() - x.min())/(x.count() -2) + return (x.sum() - x.max() - x.min()) / (x.count() - 2) else: return x.mean() + def diff_by_source(df): - #diff_by_source: input get_mark_df(), calculate the pricing by source - df = df.drop('description', 1) - df = df.set_index(['source'], append=True).apply(lambda x: x.curr_ntl * x.mark/100, axis = 1) - df = df.groupby(level =['date','identifier','source']).mean() - df = df.unstack(-1).apply(lambda x: (x-x.MANAGER), axis = 1) - return df.groupby(level = 'date').sum() + # diff_by_source: input get_mark_df(), calculate the pricing by source + df = df.drop("description", 1) + df = df.set_index(["source"], append=True).apply( + lambda x: x.curr_ntl * x.mark / 100, axis=1 + ) + df = df.groupby(level=["date", "identifier", "source"]).mean() + df = df.unstack(-1).apply(lambda x: (x - x.MANAGER), axis=1) + return df.groupby(level="date").sum() def diff_by_source_percentage(df): df = diff_by_source(df) - df = df.join(ops.get_net_navs()['endbooknav']) - df = df.apply(lambda x: (x/x.endbooknav), axis = 1) - del df['endbooknav'] + df = df.join(ops.get_net_navs()["endbooknav"]) + df = df.apply(lambda x: (x / x.endbooknav), axis=1) + del df["endbooknav"] return df + def count_sources(df): - #input get_mark_df(), plot count of each source - g2 = df.set_index('source', append=True).groupby(level = ['date','source']) + # input get_mark_df(), plot count of each source + g2 = df.set_index("source", append=True).groupby(level=["date", "source"]) # there are a good amount of Bloomberg duplicates, not a big deal but should clean them up - g2['mark'].count().unstack(-1).plot() + g2["mark"].count().unstack(-1).plot() + def alt_navs(): navs = ops.get_net_navs() df = calc_mark_diff(get_mark_df()) - end_nav, beg_nav, returns, nav_100 = pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(index = df.index, columns=df.columns) + end_nav, beg_nav, returns, nav_100 = ( + pd.DataFrame(), + pd.DataFrame(), + pd.DataFrame(), + pd.DataFrame(index=df.index, columns=df.columns), + ) for col in df.columns: end_nav[col] = df[col] + navs.endbooknav beg_nav[col] = end_nav[col].shift(1) + navs.net_flow.shift(1) beg_nav[col].iloc[0] = 12500000 - returns[col] = (end_nav[col] - navs.incentive)/beg_nav[col] -1 + returns[col] = (end_nav[col] - navs.incentive) / beg_nav[col] - 1 for i, row in returns.dropna().reset_index().iterrows(): - nav_100.iloc[i] = 100 if i == 0 else nav_100.iloc[i-1] + nav_100.iloc[i] = 100 if i == 0 else nav_100.iloc[i - 1] nav_100.iloc[i] = nav_100.iloc[i] * (1 + returns.iloc[i]) return returns, nav_100 + def annual_performance(nav_100): - perf = nav_100.groupby(pd.Grouper(freq = 'A')).last() - perf_ann = perf/perf.shift(1) - 1 - perf_ann['2013'] = perf['2013']/100-1 + perf = nav_100.groupby(pd.Grouper(freq="A")).last() + perf_ann = perf / perf.shift(1) - 1 + perf_ann["2013"] = perf["2013"] / 100 - 1 return perf_ann + def alt_nav_impact(): navs = ops.get_net_navs() df = calc_mark_diff(get_mark_df()) df = df.join(navs.endbooknav) return df - #return df.iloc[-1]/df.iloc[-1]['endbooknav'] + # return df.iloc[-1]/df.iloc[-1]['endbooknav'] -def back_test(begindate = '2013-01-01', enddate = '2018-01-01', sell_price_threshold = 200): - df = pd.read_sql_query("SELECT * FROM external_marks_mapped WHERE source IS NOT NULL", dawn_engine, - parse_dates=['date']) - df_wide = (pd.pivot_table(df, 'mark', ['identifier', 'date'], 'source').reset_index().sort_values('date')) - df_trades = pd.read_sql_query("select trade_date, identifier, price, buysell from bonds", - dawn_engine, parse_dates=['trade_date']) - df_trades.sort_values('trade_date', inplace = True) - df_sell_wide = pd.merge_asof(df_trades[df_trades.buysell == False], df_wide, left_on='trade_date', right_on='date', by='identifier').drop('date', 1) - df_long = df_sell_wide.set_index(['trade_date','identifier','price','buysell']).stack() - df_long = df_long.reset_index().rename(columns={'level_4': 'source', 0:'mark'}) - df_long['difference'] = (df_long['price'] - df_long['mark'])/df_long['mark'] +def back_test( + begindate="2013-01-01", enddate=datetime.date.today(), sell_price_threshold=200 +): + sql_string = "SELECT * FROM external_marks_mapped WHERE source IS NOT NULL" + df = pd.read_sql_query( + sql_string, + dawn_engine, + parse_dates=["date"], + index_col=["date", "identifier", "source"], + ) + df = pd.pivot_table( + df, values="mark", index=["identifier", "date"], columns="source" + ) - #filtering - df_long = df_long[df_long.identifier.str.len() == 9] - df_long = df_long[df_long.price < sell_price_threshold] - df_long = df_long[(df_long['trade_date'] > begindate) & (df_long['trade_date'] < enddate)] - df_long.loc[df_long.source == 'MANAGER','source'] = 'LMCG' + sql_string = "select trade_date, identifier, price from bonds where buysell=%s and asset_class = %s" + df_trades = pd.read_sql_query( + sql_string, dawn_engine, parse_dates=["trade_date"], params=[False, "Subprime"] + ) - return df_long + df = pd.merge_asof( + df_trades.sort_values("trade_date"), + df.reset_index().sort_values("date"), + left_on="trade_date", + right_on="date", + by="identifier", + ).drop("date", 1) -def stats(df_long, diff_threshold = 5): + df = df.set_index(["trade_date", "identifier", "price"]).stack() + df = df.reset_index().rename(columns={"level_3": "source", 0: "mark"}) + df["difference"] = (df["price"] - df["mark"]) / df["mark"] - g = df_long[df_long.difference < diff_threshold].groupby('source') + # filtering + df = df[df.identifier.str.len() == 9] + df = df[df.price < sell_price_threshold] + df = df[(df["trade_date"] > begindate) & (df["trade_date"] < enddate)] + df.loc[df.source == "MANAGER", "source"] = "LMCG" + + return df - #fit all the models at once - params = g.apply(lambda df: gls('price~mark', df).fit().params) - error = pd.DataFrame([g.difference.mean(),g.difference.std()]) - error.index = ['average', 'standard deviation'] + +def stats(df_long, diff_threshold=5): + + g = df_long[df_long.difference < diff_threshold].groupby("source") + + # fit all the models at once + params = g.apply(lambda df: gls("price~mark", df).fit().params) + error = pd.DataFrame([g.difference.mean(), g.difference.std()]) + error.index = ["average", "standard deviation"] return params, error + def pretty_plot(df_long): - #plt.switch_backend('Agg') + # plt.switch_backend('Agg') sb.set_style("whitegrid") sb.set_context("notebook") - order = ['LMCG','BROKER','BVAL','IDC','MARKIT','PRICESERVE', - 'PRICINGDIRECT','REUTERS','S&P'] - sb.set_palette(sb.hls_palette(10, l=.4, s=.8)) + order = [ + "LMCG", + "BROKER", + "BVAL", + "IDC", + "MARKIT", + "PRICESERVE", + "PRICINGDIRECT", + "REUTERS", + "S&P", + ] + sb.set_palette(sb.hls_palette(10, l=0.4, s=0.8)) - grid = sb.FacetGrid(df_long, hue='source', hue_kws={'s':[50] + [20]*9, - 'marker': ["o"]+["s"]*9, 'alpha': [1]+[.4]*9}, legend_out=True, - aspect=2.1, height= 4, hue_order=order) + grid = sb.FacetGrid( + df_long, + hue="source", + hue_kws={ + "s": [50] + [20] * 9, + "marker": ["o"] + ["s"] * 9, + "alpha": [1] + [0.4] * 9, + }, + legend_out=True, + aspect=2.1, + height=4, + hue_order=order, + ) grid.set(ylim=(0, 105), xlim=(0, 105)) - ax = grid.map(plt.scatter, 'mark', 'price').add_legend() - ax.set_axis_labels('Mark', 'sale price') - plt.plot([100, 0], [100, 0], color="black", lw=2, linestyle='solid') + ax = grid.map(plt.scatter, "mark", "price").add_legend() + ax.set_axis_labels("Mark", "sale price") + plt.plot([100, 0], [100, 0], color="black", lw=2, linestyle="solid") ax.fig.savefig("/home/serenitas/edwin/PythonGraphs/backtest.png") |